Project Objectives

• Develop mathematical algorithms that optimise breeding strategies for genetically modified laboratory animals, reducing the number of surplus animals produced.
• Create a user-friendly online tool (Multigeneration Breeding Planner) that enables researchers and facility managers to plan optimal breeding strategies without specialist software.
• Validate the algorithms through retrospective and prospective studies using real-world breeding data from collaborating animal facilities.
• Publish findings and provide open-access resources to promote widespread adoption of optimised breeding practices.

3Rs Impact

• Optimised breeding strategies can reduce the number of animals produced by up to 59% compared to suboptimal approaches.
• The tool is estimated to potentially save thousands of mice per year in Switzerland alone, with greater impact across Europe.
• Based on Swiss statistics showing approximately 1.2 million mice born annually in research facilities, even partial adoption of optimal breeding strategies could prevent the production of tens of thousands of surplus animals.

Background

Breeding genetically modified mice for research often produces large numbers of surplus animals – those whose genotypes are unsuitable for either experiments or further breeding. When crossing heterozygous animals to produce specific genotypes, Mendelian inheritance means that a significant proportion of offspring will not carry the desired genetic combination. For complex breeding schemes involving multiple genetic modifications, this problem multiplies substantially.

Currently, no widely available software helps researchers plan breeding strategies that minimise surplus animal production. Existing colony management systems focus on record-keeping rather than prospective optimisation. This gap means breeding decisions are often made without rigorous mathematical planning, resulting in more animals being produced and subsequently euthanised than necessary.

This project addresses this gap by developing the Multigeneration Breeding Planner (MBP), a software tool that models breeding outcomes mathematically and recommends strategies that produce the required experimental animals with the fewest surplus. The tool incorporates Mendelian genetics, fertility rates, litter sizes, and other biological parameters to provide probabilistic predictions of breeding outcomes.

The research team combines expertise in laboratory animal science, bioinformatics, statistics, and mathematical optimisation. Building on previous 3RCC-funded work that produced initial algorithms and a single-gene breeding planner, this project extends the capability to handle multiple genetic loci and delivers the tool as a free, web-based application accessible to all researchers.